Figures and results

Reading and tidying data

# VSG seq results
vsg_seq_results <- read_csv("data/Beaver_raw_vsg_seq_results_WT.csv")

# Sequencing alignment data
alignment_data <- read_csv("data/Beaver_VSG_seq_alignment_data.csv")

# parasitemia count data
parasitemia <- read_csv("data/parasitemia.csv") 

# tissue parasite laod data (qPCR)
parasite_load_qPCR <- read_csv("data/parasite_load_qPCR.csv")

# Tidying tissue names to be consistent and renaming VSGs to cluster names
vsg_seq_results_tidy <- vsg_rename(vsg_seq_results, 
                                   "data/cluster_reference_table.txt") %>% 
  mutate(tissue = case_when(tissue == "Brain" ~ "brain",
                            tissue == "Ear" ~ "ear",
                            tissue == "Gon" ~ "gon",
                            tissue == "Heart" ~ "heart",
                            tissue == "Lung" ~ "lung",
                            tissue == "Sub" ~ "sub",
                            tissue == "gonfat" ~ "gon",
                            tissue == "subcu" ~ "sub", 
                            tissue == "lungs" ~ "lung",
                            tissue == "GonFat" ~ "gon", 
                            tissue == "SubCu" ~ "sub",
                            TRUE ~ tissue)) %>%
  mutate(tissue_final = case_when(tissue == "ear" ~ "skin",
                            tissue == "gon" ~ "gon. fat",
                            tissue == "sub" ~ "s.c. fat",
                            TRUE ~ tissue)) %>%
  select(-...1)

# Filtering VSGs that represented less than 0.01% of parasites in a sample
results <-  vsg_seq_results_tidy %>%
  filter(Percent > 0.01) %>% 
  mutate(day = factor(day, levels = c("D6", "D10", "D14"))) %>%
  group_by(samplename) %>%
  distinct(cluster, .keep_all = TRUE) %>%
  ungroup()

# Getting rid of samples with less than 100,000 reads aligning
bad_alignment <- alignment_data %>%
  filter(reads_aligned < 100000)

results <- anti_join( results, bad_alignment, by = c("mouse", "tissue"))

# Filter VSGs that represent less than 1 parasite based on Percentage and parasitemia data
parasite_numbers <- parasite_load_qPCR %>% 
  mutate(tissue = case_when(tissue == "sc" ~ "sub", TRUE ~ tissue)) %>%
  mutate(day = str_to_upper(day),
         mouse = str_to_upper(mouse)) %>%
  select(mouse,day,tissue, total_parasites_in_sample)

results <- left_join(results, parasite_numbers) %>%
  mutate(parasites = (Percent/100)*total_parasites_in_sample)

vsgs_tossed_from_parasitemia<- results %>%
  filter(parasites < 1) %>%
  group_by(samplename) %>%
  count()

results <- results %>%
  filter(parasites > 1 | is.na(parasites) == TRUE)

# Tidying and giving mice new figure names (1-12)

results <- results %>%
  mutate(day = factor(day, levels = c("D6", "D10", "D14"))) %>%
  mutate(mouse_final = case_when(mouse == "M12" ~ "1", 
                                 mouse == "M27" ~ "2",
                                 mouse == "M32" ~ "3",
                                 mouse == "M33" ~ "4",
                                 mouse == "M30" ~ "5",
                                 mouse == "M36" ~ "6",
                                 mouse == "M4" ~ "7",
                                 mouse == "M6" ~ "8",
                                 mouse == "M2" ~ "9",
                                 mouse == "M34" ~ "10",
                                 mouse == "M35" ~ "11",
                                 mouse == "M3" ~ "12")) %>%
  mutate(mouse_final = as.numeric(mouse_final))

# Need one data frame with all VSGs represented in each space even if its not detected there for plotting purposes. 
results_all_combos <- vsg_expand(vsg_seq_results_tidy, "samplename", "cluster") %>%
  separate(samplename, c("mouse", "day", "tissue"), sep = "_", remove = FALSE) %>%
   mutate(tissue = case_when(tissue == "Brain" ~ "brain",
                            tissue == "Ear" ~ "ear",
                            tissue == "Gon" ~ "gon",
                            tissue == "Heart" ~ "heart",
                            tissue == "Lung" ~ "lung",
                            tissue == "Sub" ~ "sub",
                            tissue == "gonfat" ~ "gon",
                            tissue == "subcu" ~ "sub", 
                            tissue == "lungs" ~ "lung",
                            tissue == "GonFat" ~ "gon", 
                            tissue == "SubCu" ~ "sub",
                            TRUE ~ tissue)) %>%
  mutate(tissue_final = case_when(tissue == "ear" ~ "skin",
                            tissue == "gon" ~ "gon. fat",
                            tissue == "sub" ~ "s.c. fat",
                            TRUE ~ tissue)) %>%
  anti_join(bad_alignment, by = c("mouse", "tissue"))

Main figures

Figure 1

A

top_11_VSGs <- results %>% 
  select(day, tissue, cluster, Percent) %>%
  select(-tissue, -day) %>%
  group_by(cluster) %>%
  summarize(sum = max(Percent)) %>%
  slice_max(sum, n = 11) %>%
  mutate(color = cluster,
         top_11_whole = "top") 

stacked_bar_plot_dat <- results %>%
  mutate(mouse = factor(mouse, levels = c("M12","M27", "M32", 
                                          "M33", "M30", "M36", 
                                          "M4", "M6", "M2",  
                                          "M34", "M35", "M3" ))) %>%
  left_join(top_11_VSGs) %>%
  mutate(color = case_when(is.na(color) == TRUE ~ "other",
                            TRUE ~ color )) %>%
  mutate(day_tis = case_when(tissue_final == "blood" ~ str_c(day, tissue_final, sep = " "), 
                             TRUE ~ tissue_final)) %>%
  mutate(day_tis = factor(day_tis, levels = c("D6 blood", "D10 blood", "D14 blood",
                                              "brain","skin","gon. fat","heart","lung","s.c. fat"))) %>%
  mutate(color = paste("VSG", color, sep = " "), 
         color = case_when( color == "VSG Cluster 56" ~ "AnTat1.1",
                              color == "VSG Cluster 1830" ~ "VSG-421",
                              TRUE ~ color),
         color = fct_reorder(color, sum , .desc = TRUE),
         color = fct_relevel(color, "VSG Cluster 294", after = 2), 
         mouse_final_2 = paste("Mouse", mouse_final, sep = " "),
         mouse_final_2 = fct_reorder(mouse_final_2, mouse_final)) %>% 
  group_by(mouse_final_2, day, tissue_final) %>%
  mutate(percent_final = (Percent/sum(Percent))*100)

stacked_bar_plot <- stacked_bar_plot_dat %>%
  ggplot(aes(x = day_tis, y = percent_final, fill = color)) +
  geom_col(color = "black") +
  facet_wrap("mouse_final_2", nrow = 3) + 
  theme_pubr(legend = "right", base_size =20) +
  theme(axis.text.x = element_text(angle = 90, size = 8)) +
  scale_fill_manual(values = color_palette_12) +
  xlab("") +
  ylab("Percent of expression")
stacked_bar_plot 

ggsave("figures/figure_1A.pdf", stacked_bar_plot,
       device = "pdf", width = 10, height = 6, units = "in")

B

D10_mouse_VSG_counts <- results %>%
  filter(mouse_final == "5" | mouse_final == "6" | mouse_final == "7" | mouse_final == "8",
         day == "D10") %>%
  mutate(space = case_when(tissue == 'blood' ~ 'blood only', TRUE ~ 'tissues only')) %>%
  group_by(space) %>%
  distinct(cluster, mouse_final, day) %>%
  ungroup() %>%
  group_by(mouse_final, cluster) %>%
  add_tally() %>%
  mutate(space = case_when(n == 2 ~ 'shared', TRUE ~ space)) %>%
  ungroup() %>% 
  group_by(mouse_final, space) %>%
  distinct(cluster, space) %>%
  count(name = "VSGs",sort = TRUE) %>%
  ungroup() %>%
  group_by(mouse_final) %>%
  mutate(total_vsgs = sum(VSGs)) %>%
  ungroup() %>%
  mutate(day = "Day 10")

D6_mouse_VSG_counts <- results %>%
  filter(mouse_final == "1" | mouse_final == "2" | mouse_final == "3" 
         | mouse_final == "4") %>% 
  mutate(space = case_when(tissue == 'blood' ~ 'blood only', TRUE ~ 'tissues only')) %>%
  group_by(space) %>%
  distinct(cluster, mouse_final, day) %>%
  ungroup() %>%
  group_by(mouse_final, cluster) %>%
  add_tally() %>%
  mutate(space = case_when(n == 2 ~ 'shared', TRUE ~ space)) %>%
  ungroup() %>% 
  group_by(mouse_final, space) %>%
  distinct(cluster, space) %>%
  count(name = "VSGs",sort = TRUE) %>%
  ungroup() %>%
  group_by(mouse_final) %>%
  mutate(total_vsgs = sum(VSGs)) %>%
  ungroup() %>%
  mutate(day = "Day 6")

D14_mouse_VSG_counts <- results %>%
  filter(mouse_final == "9" | mouse_final == "10" | mouse_final == "11" | mouse_final == "12",
         day == "D14") %>%
  mutate(space = case_when(tissue == 'blood' ~ 'blood only', TRUE ~ 'tissues only')) %>%
  group_by(space) %>%
  distinct(cluster, mouse_final, day) %>%
  ungroup() %>%
  group_by(mouse_final, cluster) %>%
  add_tally() %>%
  mutate(space = case_when(n == 2 ~ 'shared', TRUE ~ space)) %>%
  ungroup() %>% 
  group_by(mouse_final, space) %>%
  distinct(cluster, space) %>%
  count(name = "VSGs",sort = TRUE) %>%
  ungroup() %>%
  group_by(mouse_final) %>%
  mutate(total_vsgs = sum(VSGs)) %>%
  ungroup() %>%
  mutate(day = "Day 14")

mouse_VSGs_count_plot <- D6_mouse_VSG_counts %>%
  bind_rows(D10_mouse_VSG_counts, D14_mouse_VSG_counts) %>%
  mutate(mouse_final = as.character(mouse_final)) %>%
  mutate(
         day = factor(day, levels = c("Day 6", "Day 10", "Day 14")),
         percent_vsgs = (VSGs/total_vsgs)*100) %>%
  mutate(mouse_final = as.numeric(mouse_final)) %>%
  ggplot(aes(x = mouse_final, y = percent_vsgs))+
  geom_col(aes(fill = space))+
  facet_wrap("day", scales = "free_x") + 
  ylab("Percent of VSGs") + 
  xlab("Mouse") +
  theme_pubr(base_size = 20) +
  theme(legend.position = "top") +
  scale_fill_manual(values = color_palette_3)

mouse_VSGs_count_plot

ggsave("figures/figure_1B.pdf", mouse_VSGs_count_plot,
       device = "pdf", width = 8, height = 6, units = "in")

C

counts_per_sample <- results %>%
  vsg_count(day, mouse_final, tissue_final) %>%
  mutate(space = case_when(tissue_final != "blood" ~ "tissues", 
                           TRUE ~ tissue_final)) 

counts_by_space_t_test_dat <- counts_per_sample %>% 
  group_by(day) %>%
  pairwise_t_test(vsg_count ~ space, p.adjust.method = "BH") %>%
  add_xy_position(x = "day", dodge = 0.75)

counts_by_space_plot <- counts_per_sample %>%
  ggplot(aes(x = day, y = vsg_count, group = interaction(day, space))) + 
  geom_boxplot(aes(fill = space), alpha = .5, width =.75) +
  geom_point(aes(color = space),size = 2.5, position = position_jitterdodge(.1)) + 
  theme_pubr(legend = "right", base_size = 20) +
  xlab("Time post infection (days)") +
  ylab("Number of VSGs") +
  scale_fill_manual(values = color_palette_12) +
  scale_color_manual(values = color_palette_12) +
  add_pvalue(counts_by_space_t_test_dat, 
             label = "p.adj.signif",
             xmin = "xmin", 
             xmax = "xmax")
counts_by_space_plot

ggsave("figures/figure_1C.pdf", counts_by_space_plot,
       device = "pdf", width = 8, height = 6, units = "in")

D

counts_by_sample_dunn_test<- counts_per_sample %>%
  group_by(day) %>%
  dunn_test(vsg_count ~ tissue_final, p.adjust.method = "BH" ) %>%
  add_xy_position(step.increase = 0.05)

counts_by_sample_boxplot <- results %>%
  filter(mode == "IV",
         strain == "WT") %>%
  vsg_count(day, mouse, tissue_final) %>%
  ggplot(aes(x = tissue_final, y = vsg_count)) + 
  facet_wrap( vars(day))  +
  geom_boxplot(outlier.size = 0) +
  geom_dotplot(binaxis='y', stackdir= "center", stackratio = .8, dotsize=.32) +
  xlab("") +
  ylab("Number of VSGs") +
  theme_pubr(base_size = 20) +
  theme(axis.text.x = element_text(angle = 90,
                                 hjust = 1, 
                                 size = 12, 
                                 color = 'black'),
        legend.position = "right") +
  stat_pvalue_manual(counts_by_sample_dunn_test, 
             label = "p.adj.signif",
             xmin = "xmin", 
             xmax = "xmax", 
             hide.ns = TRUE,
             size = 3.8, 
             tip.length = 0.02,
             bracket.nudge.y = -30)
counts_by_sample_boxplot

ggsave("figures/figure_1D.pdf", counts_by_sample_boxplot,
       device = "pdf", width = 8, height = 6, units = "in")

Figure 2

B

vsg_percent_unique <- results %>%
  select(cluster, mouse_final, day, tissue_final) %>%
  group_by(mouse_final, cluster) %>%
  add_tally() %>%
  filter(n == 1) %>% 
  group_by(mouse_final, day, tissue_final) %>%
  count() %>%
  rename(number_unique = n) %>%
  right_join(counts_per_sample) %>%
  filter(mouse_final %in% day10_mice & day == "D10" |
           mouse_final %in% day14_mice & day == "D14") %>%
  mutate(percent_unique = (number_unique / vsg_count)*100,
         ratio_unique = number_unique / vsg_count,
         not_unique = vsg_count - number_unique)%>%
  mutate(percent_unique= replace_na(percent_unique, 0)) 

vsg_percent_unique_t_test_dat <- vsg_percent_unique %>%
  group_by(day) %>%
  pairwise_t_test(percent_unique ~ tissue_final, p.adjust.method = "BH") %>%
  add_xy_position()

vsg_percent_unique_graph <- vsg_percent_unique %>% 
  ggplot(aes(x = tissue_final, y = percent_unique)) +
  geom_boxplot(outlier.size = 0) +
  geom_dotplot(binaxis='y', stackdir= "center", stackratio = .8, dotsize=.4) + 
  facet_wrap("day") +
  xlab("") +
  ylab("Percent of VSGs unique to only one space within each mouse") +
  theme_pubr(base_size = 20) +
  #scale_y_continuous(limits = c(0,100)) +
  theme(axis.text.x = element_text(angle = 90,
                                 hjust = 1, 
                                 size = 12, 
                                 color = 'black'),
        legend.position = "right",
        legend.background = element_rect(fill = NULL, color = NULL)) +
 stat_pvalue_manual(vsg_percent_unique_t_test_dat, 
             label = "p.adj.signif",
             xmin = "xmin", 
             xmax = "xmax", 
             hide.ns = TRUE)

vsg_percent_unique_graph

ggsave("figures/figure_2B.pdf", vsg_percent_unique_graph,
       device = "pdf", width = 8, height = 6, units = "in")

C

next_dominant_vsgs <- results %>%
  filter(cluster == "Cluster 294" | cluster == "Cluster 1831" | 
           cluster == "Cluster 504" | cluster == "Cluster 4")%>% 
  select(mouse, cluster, mouse_final) %>%
  merge(results_all_combos, by = c('mouse','cluster')) %>%
  mutate(Percent = case_when(Percent < .01 ~ 0,
                             TRUE~ Percent)) %>%
  mutate(Percent = Percent + .001) %>%
  mutate(day = factor(day, levels = c("D6", "D10", "D14"))) %>%
  distinct()

early_cluster_vsg_expression <- next_dominant_vsgs %>%
    mutate(tissue_final = case_when(tissue == "ear" ~ "skin",
                            tissue == "gon" ~ "gon. fat",
                            tissue == "sub" ~ "s.c. fat",
                            TRUE ~ tissue)) %>%
  filter(mouse %in% day6_mice_IV_wt & day == "D6" |
         mouse %in% day10_mice_IV_wt & day == "D10"|
         mouse %in% day14_mice_IV_wt & day == "D14") %>%
  separate(day, c("day1","day_num"), sep = "D", convert = TRUE, remove = FALSE) %>%
  mutate(day_num_char = as.character(day_num)) %>%
  mutate(day_num_char = factor(day_num_char, levels = c("6","10", "14"))) %>%
  select(-day1) %>% 
  filter(cluster != "Cluster 4") %>%
  mutate(space = case_when(tissue == "blood" ~ "blood", 
                           TRUE ~ "tissues"),
         cluster = factor(cluster, levels = c("Cluster 294", "Cluster 504", 
                                              "Cluster 1831"))) %>%
  ggplot(aes(x = day_num_char, y = log10(Percent),
             group = interaction(day_num_char,space))) +
  geom_boxplot(aes(fill = space), alpha = .5, width = .75) +
  geom_point(aes(color = tissue_final), size = 2, 
             position = position_jitterdodge(jitter.width = .1)) +
  theme_pubr(legend = "right", base_size = 20) +
  facet_wrap("cluster", nrow =1, shrink = F) +
  xlab("Time post infection (days)") +
  ylab("Percent of parasites expressing the starting VSG") +
  scale_color_manual(values = color_palette_12) +
  scale_fill_manual(values = color_palette_12) +
  scale_y_continuous(limits = c(-3,2)) +
  geom_hline(yintercept = -2, linetype = 2) +
  scale_y_continuous(labels = c("n.d.", "0.01%","0.1%", "1.0%", "10%", "100%")) +
  guides(color = guide_legend(title = "Tissue"),
         fill = guide_legend(title = "Space"))
early_cluster_vsg_expression

ggsave("figures/figure_2C.pdf", early_cluster_vsg_expression,
       device = "pdf", width = 8, height = 6, units = "in")

Figrue 3

A

starting_vsgs <- results %>%
  filter(day == "D6", tissue == "blood") %>%
  vsg_max(mouse) %>% 
  select(mouse, cluster, mouse_final) %>%
  merge(results_all_combos, by = c('mouse','cluster')) %>%
  mutate(Percent = case_when(Percent < .01 ~ 0,
                             TRUE~ Percent)) %>%
  mutate(Percent = Percent + .001) %>%
  mutate(day = factor(day, levels = c("D6", "D10", "D14"))) %>%
  mutate(tissue_final = case_when(tissue == "ear" ~ "skin",
                            tissue == "gon" ~ "gon. fat",
                            tissue == "sub" ~ "s.c. fat",
                            TRUE ~ tissue))

starting_vsg_espression_3_dat <- starting_vsgs %>%
  filter(mouse %in% day6_mice_IV_wt & day == "D6" |
         mouse %in% day10_mice_IV_wt & day == "D10"|
         mouse %in% day14_mice_IV_wt & day == "D14") %>%
  separate(day, c("day1","day_num"), sep = "D", convert = TRUE, remove = FALSE) %>%
  mutate(day_num_char = as.character(day_num)) %>%
  mutate(day_num_char = factor(day_num_char, levels = c("6","10", "14"))) %>%
  select(-day1) %>% 
  mutate(space = case_when(tissue == "blood" ~ "blood", 
                           TRUE ~ "tissues")) %>%
  mutate(expression = case_when(Percent == .001 ~ "N", 
                                TRUE ~ "Y"),
         day = factor(day_num, levels = c("6", "10", "14"))) 


wilcox_test_starting_vsgs <- starting_vsg_espression_3_dat %>%
  mutate(Percent = Percent - .001) %>%
  group_by(day) %>%
  pairwise_wilcox_test(Percent ~ space, p.adjust.method = "BH") %>%
  add_xy_position() %>%
  mutate(y.position = log10(y.position)) %>%
  mutate(xmin = case_when(day == "6" ~ .75, 
                          day == "10" ~ 1.75, 
                          day == "14" ~ 2.75)) %>%
  mutate(xmax = case_when(day == "6" ~ 1.25, 
                          day == "10" ~ 2.25,
                          day == "14" ~ 3.25)) %>%
  mutate(y.position = case_when(day == "6" ~ 2.4, 
                          day == "10" ~ 2.2,
                          day == "14" ~ .7))

starting_vsg_espression_3 <- starting_vsg_espression_3_dat %>%
  ggplot(aes(x = day, y = log10(Percent), group = interaction(day, space))) +
  geom_boxplot(aes(fill = space), alpha = .5, width = 1) +
  geom_point(aes(color = tissue_final), size = 2.5, position =
               position_jitterdodge(jitter.width = .1, dodge.width = 1)) +
  theme_pubr(legend = "right", base_size = 20) +
  xlab("Time post infection (days)") +
  ylab("Percent of parasites\nexpressing the starting VSG") +
  scale_color_manual(values = color_palette_12) +
  scale_fill_manual(values = color_palette_12) +
  stat_pvalue_manual(wilcox_test_starting_vsgs, 
             label = "p.adj.signif",
             xmin = "xmin", 
             xmax = "xmax") +
  ylim(-3,2) +
  geom_hline(yintercept = -2, linetype = 2) +
  scale_y_continuous(labels = c("n.d.", "0.01%","0.1%", 
                                "1.0%", "10%", "100%", "1000%")) +
  guides(color = guide_legend(title = "Tissue"),
         fill = guide_legend(title = "Space"))
starting_vsg_espression_3

ggsave("figures/figure_3A.pdf", starting_vsg_espression_3,
       device = "pdf", width = 8, height = 6, units = "in")

B

#Facs data

Figure 4

# Loading AID-/- sequencing data
# Data from the first flow cell, which was with some of the WT samples
AID_raw_results_1 <- read_csv("data/AID data/Beaver_raw_vsg_seq_results_AID_1.csv") %>%
  vsg_rename("data/cluster_reference_table.txt") %>% 
  mutate(tissue = case_when(tissue == "Brain" ~ "brain",
                            tissue == "Ear" ~ "ear",
                            tissue == "Gon" ~ "gon",
                            tissue == "Heart" ~ "heart",
                            tissue == "Lung" ~ "lung",
                            tissue == "Sub" ~ "sub",
                            tissue == "gonfat" ~ "gon",
                            tissue == "subcu" ~ "sub", 
                            tissue == "lungs" ~ "lung",
                            tissue == "GonFat" ~ "gon", 
                            tissue == "SubCu" ~ "sub",
                            TRUE ~ tissue)) %>%
  mutate(tissue_final = case_when(tissue == "ear" ~ "skin",
                            tissue == "gon" ~ "gon. fat",
                            tissue == "sub" ~ "s.c. fat",
                            TRUE ~ tissue)) %>%
  select(-mode,
         -...1) 

# Data from the second AID flow cell
AID_raw_results_2 <- read_csv("data/AID data/Beaver_raw_vsg_seq_results_AID_2.txt") %>%
  mutate(strain = "AID",
         experiment = "2") %>% 
  vsg_rename("data/AID data/AID_cluster_reference_table.txt") %>%
  mutate(tissue = case_when(tissue == "ear" ~ "skin",
                            tissue == "gon" ~ "gon. fat",
                            tissue == "gonadal" ~ "gon. fat",
                            tissue == "lungs" ~ "lung",
                            tissue == "sc" ~ "s.c. fat",
                            tissue == "scfat" ~ "s.c. fat",
                            TRUE ~ tissue),
         tissue_final = tissue) %>%
  mutate(day = toupper(day))

# Combinig all AID VSG seq results 
AID_raw_results <- rbind(AID_raw_results_1, AID_raw_results_2)

# Filtering out VSGs less than 0.01% of parasites in a sample
AID_results <- AID_raw_results %>% 
  filter(Percent > 0.01) %>% 
  mutate(day = factor(day, levels = c("D6", "D10", "D14"))) %>%
  group_by(samplename) %>%
  distinct(cluster, .keep_all = TRUE) %>%
  ungroup() %>%
  mutate(mouse_final = paste(mouse, strain, sep = "_"))

AID_results_all_combos <- vsg_expand(AID_results, "samplename", "cluster") %>%
  separate(samplename, c("mouse", "day", "tissue"), sep = "_", remove = FALSE) %>%
  mutate(strain = "AID")

# Combining WT and AID data
wt_results <- results %>%
  select(-mode, -total_parasites_in_sample, -parasites) %>%
  mutate(experiment = "1")

wt_results_all_combos <-  vsg_expand(wt_results, "samplename", "cluster") %>%
  separate(samplename, c("mouse", "day", "tissue"), sep = "_", remove = FALSE) %>%
  mutate(strain = "WT") %>%
  anti_join(bad_alignment, by = c("mouse", "tissue"))

wt_and_aid_results <- rbind(AID_results, wt_results)

# Need to create a data frame with all VSGs represented in each sample
wt_and_aid_results_all_combos <- rbind(AID_results_all_combos, wt_results_all_combos) %>%
  mutate(tissue = case_when(tissue == "Brain" ~ "brain",
                            tissue == "Ear" ~ "ear",
                            tissue == "Gon" ~ "gon",
                            tissue == "Heart" ~ "heart",
                            tissue == "Lung" ~ "lung",
                            tissue == "Sub" ~ "sub",
                            tissue == "gonfat" ~ "gon",
                            tissue == "subcu" ~ "sub", 
                            tissue == "lungs" ~ "lung",
                            tissue == "GonFat" ~ "gon", 
                            tissue == "SubCu" ~ "sub",
                            TRUE ~ tissue)) %>%
  mutate(tissue_final = case_when(tissue == "ear" ~ "skin",
                            tissue == "gon" ~ "gon. fat",
                            tissue == "sub" ~ "s.c. fat",
                            TRUE ~ tissue))

A

wt_and_aid_starting_vsgs <- wt_and_aid_results %>%
  filter(day == "D6", tissue == "blood") %>%
  vsg_max(mouse) %>%
  select(mouse, cluster) %>%
  merge(wt_and_aid_results_all_combos, by = c('mouse','cluster')) %>%
  mutate(Percent = case_when(Percent < .01 ~ 0,
                             TRUE~ Percent)) %>%
  mutate(Percent = Percent + .001) %>%
  mutate(space = case_when(tissue != "blood" ~ "tissues", 
                           TRUE ~ tissue)) %>%
  mutate(strain_space = paste(strain, space, sep = " ")) %>%
  mutate(strain_space = factor(strain_space, levels = c("WT blood","WT tissues","AID blood","AID tissues"))) %>%
  mutate(day = toupper(day))

wt_and_aid_starting_vsg_espression <- wt_and_aid_starting_vsgs %>%
  separate(day, c("day1","day_num"), sep = "D", convert = TRUE, remove = FALSE) %>%
  mutate(day_num_char = as.character(day_num)) %>%
  mutate(day_num_char = factor(day_num_char, levels = c("6","10", "14"))) %>%
  select(-day1) %>% 
  filter(day != "D10") %>% 
  mutate(strain = factor(strain, levels = c("WT", "AID" ))) %>%
  ggplot(aes(x = day_num_char, y = log10(Percent), group = interaction(day_num_char, space))) +
  geom_boxplot(aes(fill = space), alpha = .5, width = .75) +
  geom_point(aes(color = space), size = 2.5, position = position_jitterdodge(jitter.width = .1)) +
  theme_pubr(legend = "right", base_size = 20) +
  xlab("Time post infection (days)") +
  ylab("Percent of parasites\nexpressing the starting VSG") +
  scale_color_manual(values = red_blue) +
  scale_fill_manual(values = red_blue) +
  ylim(-3,2) +
  facet_wrap("strain") +
  scale_y_continuous(labels = c("n.d.", "0.01%","0.1%", "1%", "10%", "100%")) +
  geom_hline(yintercept = -2, linetype = 2) 

wt_and_aid_starting_vsg_espression

ggsave("figures/figure_4A.pdf", wt_and_aid_starting_vsg_espression,
       device = "pdf", width = 8, height = 6, units = "in")

B

wt_and_aid_vsg_counts <- wt_and_aid_results %>% 
  vsg_count(mouse_final, day, tissue, strain) %>% 
  mutate(space = case_when(tissue != "blood" ~ "tissues", 
                           TRUE ~ tissue)) %>%
  separate(day, c("day1","day_num"), sep = "D", convert = TRUE, remove = FALSE) %>%
  mutate(day_num_char = as.character(day_num)) %>%
  mutate(day_num_char = factor(day_num_char, levels = c("6","10", "14"))) %>%
  select(-day1) %>% 
  mutate(strain = factor(strain, levels = c("WT", "AID"))) %>%
  filter(day!= "D10") 

wt_and_aid_counts_t_test <- wt_and_aid_vsg_counts %>% 
  filter(day == "D14") %>%
 # mutate(test = paste(strain, day, space, sep = "_")) %>%
  group_by(space, day_num_char) %>%
  pairwise_t_test(vsg_count ~ strain, p.adjust.method = "BH") 

wt_and_aid_vsg_count_plot <- wt_and_aid_vsg_counts %>%
  ggplot(aes(x = day_num_char, y = vsg_count, group = interaction(day, space))) + 
  geom_boxplot(aes(fill = space), alpha = .5, width =.75) +
  geom_point(aes(color = space),size = 2.5, position = position_jitterdodge(.1)) + 
  theme_pubr(legend = "right", base_size = 20) +
  xlab("Time post infection (days)") +
  ylab("Number of VSGs") +
  facet_wrap("strain") +
  scale_fill_manual(values = red_blue) +
  scale_color_manual(values = red_blue) + 
  scale_y_continuous(limits = c(0,275), n.breaks = 7)
wt_and_aid_vsg_count_plot

ggsave("figures/figure_4B.pdf", wt_and_aid_vsg_count_plot,
       device = "pdf", width = 8, height = 6, units = "in")

Supplemental figures

S.5

# Loading Elisa data
IgG_dat <- read_csv("data/elisa_data/BZEX44_09012020_IgG_lower_dilution_R.csv") %>%
  mutate(ab = "IgG") 

IgG_dat <- IgG_dat %>%
  separate(Sample.Name, c("day", "mouse", "strain"), sep = "_") %>%
  filter(!str_detect(day, "STD"),
         mouse != "Blank") %>%
  select(mouse, day, strain, ab, Conc_mg.ml) %>%
  group_by(mouse, day, strain, ab) %>% 
  summarise(mean_concentraion_mg_ml = mean(Conc_mg.ml))
  

IgM_dat <- read_csv("data/elisa_data/BZEX44_09042020_IgM_R.csv") %>%
  mutate(ab = "IgM") 

IgM_dat <- IgM_dat %>%
  separate(Sample.Name, c("day", "mouse", "strain"), sep = "_") %>%
  filter(!str_detect(day, "STD")) %>%
  select(mouse, day, strain, ab, Conc_mg.ml) %>%
  group_by(mouse, day, strain, ab) %>% 
  summarise(mean_concentraion_mg_ml = mean(Conc_mg.ml))

# Merge IgG and IgM data
elisa_dat <- rbind(IgG_dat, IgM_dat)

elisa_plot <- elisa_dat %>%
  filter(strain == "WT" | strain == "AID") %>%
  separate(day, c("day1","day_num"), sep = "D", convert = TRUE, remove = FALSE) %>%
  select(-day1) %>%
  mutate(ab = factor(ab, levels = c("IgM", "IgG"))) %>%
  ggplot(aes(x = day_num, y = mean_concentraion_mg_ml, color = ab, fill = ab)) +
  geom_point(position = position_dodge2(1)) +
  theme_pubr(legend = "right", base_size = 20) +
  facet_wrap("strain") +
  stat_summary(aes(group=ab),fun=mean,geom="line", size = 1.2) +
  scale_color_manual(values = red_blue) +
  scale_fill_manual(values = red_blue) +
  scale_x_continuous( breaks = c(0,6,10,14)) + 
  ylab("Concentration of antibody (mg/ml)") +
  xlab("Time post infection (days)")
elisa_plot

ggsave("figures/figure_S5.pdf", elisa_plot,
       device = "pdf", width = 8, height = 6, units = "in")

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ggprism_1.0.3    rstatix_0.7.0    AICcmodavg_2.3-1 scales_1.1.1    
##  [5] ggpubr_0.4.0     forcats_0.5.1    stringr_1.4.0    dplyr_1.0.7     
##  [9] purrr_0.3.4      readr_2.0.2      tidyr_1.1.4      tibble_3.1.5    
## [13] ggplot2_3.3.5    tidyverse_1.3.1 
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-153           fs_1.5.0               bit64_4.0.5           
##  [4] lubridate_1.7.10       httr_1.4.2             tools_4.1.1           
##  [7] backports_1.2.1        bslib_0.3.0            utf8_1.2.2            
## [10] R6_2.5.1               DBI_1.1.1              colorspace_2.0-2      
## [13] raster_3.4-13          withr_2.4.2            sp_1.4-5              
## [16] vsgseqtools_0.0.0.9000 tidyselect_1.1.1       bit_4.0.4             
## [19] curl_4.3.2             compiler_4.1.1         cli_3.0.1             
## [22] rvest_1.0.1            xml2_1.3.2             labeling_0.4.2        
## [25] unmarked_1.1.1         sass_0.4.0             digest_0.6.28         
## [28] foreign_0.8-81         rmarkdown_2.11         rio_0.5.27            
## [31] pkgconfig_2.0.3        htmltools_0.5.2        highr_0.9             
## [34] dbplyr_2.1.1           fastmap_1.1.0          rlang_0.4.11          
## [37] readxl_1.3.1           VGAM_1.1-6             rstudioapi_0.13       
## [40] farver_2.1.0           jquerylib_0.1.4        generics_0.1.0        
## [43] jsonlite_1.7.2         vroom_1.5.5            zip_2.2.0             
## [46] car_3.0-11             magrittr_2.0.1         Matrix_1.3-4          
## [49] Rcpp_1.0.7             munsell_0.5.0          fansi_0.5.0           
## [52] abind_1.4-5            lifecycle_1.0.1        stringi_1.7.5         
## [55] yaml_2.2.1             carData_3.0-4          MASS_7.3-54           
## [58] plyr_1.8.6             parallel_4.1.1         crayon_1.4.1          
## [61] lattice_0.20-45        haven_2.4.3            splines_4.1.1         
## [64] hms_1.1.1              knitr_1.34             pillar_1.6.3          
## [67] ggsignif_0.6.3         codetools_0.2-18       stats4_4.1.1          
## [70] reprex_2.0.1           glue_1.4.2             evaluate_0.14         
## [73] data.table_1.14.2      modelr_0.1.8           vctrs_0.3.8           
## [76] tzdb_0.1.2             cellranger_1.1.0       gtable_0.3.0          
## [79] assertthat_0.2.1       xfun_0.26              openxlsx_4.2.4        
## [82] xtable_1.8-4           broom_0.7.9            survival_3.2-13       
## [85] ellipsis_0.3.2

 

---

By Alex Beaver for the Mugnier Lab

abeaver3@jhmi.edu